Unmanned aerial vehicle flight path planning method based on heuristic algorithm and fault regression
By combining heuristic algorithms and fail regression prediction, and using particle swarm optimization algorithm to plan the drone patrol path, the problem that the existing technology cannot prioritize the handling of equipment with high failure probability is achieved, and more efficient fault detection and processing is achieved.
CN119717873APending Publication Date: 2025-03-28CHANGSHAN COUNTY POWER SUPPLY CO OF STATE GRID ZHEJIANG ELECTRIC POWER CO LTD
1 Cites 0 Cited by
Patent Information
- Application Number
- CN202410713330.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-04
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
The existing UAV flight route planning methods cannot prioritize inspection of equipment with high probability of failure, resulting in delays in fault discovery and processing time.
Method used
The drone flight route planning method based on heuristic algorithm and fail regression is adopted. Through particle swarm optimization algorithm combined with fail regression prediction, the drone patrol path of the fault route is planned, and equipment with high failure probability is given priority.
Benefits of technology
It effectively avoids fault detection and processing time delays, improves operation and maintenance efficiency and response speed, and ensures the accuracy and completeness of inspection results.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses an unmanned aerial vehicle flight path planning method based on a heuristic algorithm and fault regression, which combines a heuristic path planning algorithm with fault regression prediction, and plans an inspection path of an unmanned aerial vehicle to a fault path through one particle swarm optimization algorithm of the heuristic algorithm. Each particle represents a potential inspection path, in each iteration, the particles adjust own flight paths according to own experience and group experience, fault discovery and processing time delay are avoided, the overall operation and maintenance efficiency and response speed are ensured, the state of the unmanned aerial vehicle in the flight path is synchronously monitored, and the unmanned aerial vehicle flight path tracking accuracy is improved. And when the unmanned aerial vehicle is judged to be abnormal, timely processing and synchronously updating the inspection path to ensure the accuracy and integrity of the inspection result.
Need to check novelty before this filing date? Find Prior Art